Designing Multi-Source Citation in Generative AI : Enhancing UX and Transparency in Generative AI

Generative artificial intelligence (GenAI) has recently seen unprecedented growth andevolution. Enabling it to produce human like media unlike any other AI. These recent advancements havepropelled GenAI into mainstream adoption across a myriad of different domains. This has led to concernsbeing raised regarding the transparency, bias and misinformation of such services. And has thus, spurreda plethora of research into how to combat the biases within GenAI that reflects and perpetuates societalprejudice. However, existing approaches are largely technical and thus neglect the sociotechnicaldimension. Design Science Research does however provide a viable way to approach the context from asociotechnical perspective as it focuses on a user centred perspective. This study explores how multisource citation, a well-established method for enhancing credibility and transparency, can beimplemented into GenAI to improve transparency and the user experience. By leveraging DSR, this studyaims to develop deep insights into how multi-source citations should be designed into GenAI according tothe end users. Addressing the sociotechnical gap in GenAI research.